In-Situ Data-Driven Time-Dependent Durability Forecasting of Prefabricated Components Made with Recycled Aggregate Concrete

To achieve proactive preventive maintenance of green and low-carbon infrastructure, this study systematically investigated the long-term durability and resistance degradation of prefabricated recycled aggregate concrete (RAC) bridge components. A 80-month multi-field coupled damage experiment under sustained flexural loading, natural atmospheric exposure, and chloride drying-wetting cycles was conducted, and an in-situ physical exposure and multi-source data-driven Support Vector Regression (SVR) dynamic surrogate model was established. Results indicate a prominent time-dependent ebb-and-flow mechanism of degradation drivers: environmental and stress boundaries dominate the early stage, whereas the material replacement rate ($R_r$) surges to become the absolute dominant driving variable (36.8%) in the ultra-long term ($t=80 \mathrm{months}$), proving the cumulative dominance of recycled aggregates. Concurrently, the residual capacity exhibits a distinct two-stage decay characterized by a 10% critical reinforcement mass loss threshold, beyond which the degradation rate of RAC100 accelerates to 1.63 times that of conventional concrete. Driven by the multi-stage injection of in-situ experimental inspection data (surface crack profiling, 2D spatial chloride profiles, and rebar mass loss), the SVR network successfully achieves a collapse-like convergence of the remaining useful life (RUL) confidence interval, precisely locking the RUL of the RAC100 component at 34.5 years within a 1.4-year error margin. This framework provides critical algorithmic support for the life-cycle safety paradigm shift in low-carbon structures.